Overview

Dataset statistics

Number of variables18
Number of observations12695
Missing cells0
Missing cells (%)0.0%
Duplicate rows867
Duplicate rows (%)6.8%
Total size in memory2.3 MiB
Average record size in memory193.6 B

Variable types

Categorical8
Numeric10

Alerts

Dataset has 867 (6.8%) duplicate rowsDuplicates
no_of_previous_bookings_not_canceled is highly overall correlated with repeated_guestHigh correlation
repeated_guest is highly overall correlated with no_of_previous_bookings_not_canceledHigh correlation
no_of_adults is highly imbalanced (52.8%)Imbalance
no_of_children is highly imbalanced (76.7%)Imbalance
type_of_meal_plan is highly imbalanced (50.2%)Imbalance
required_car_parking_space is highly imbalanced (79.2%)Imbalance
repeated_guest is highly imbalanced (83.0%)Imbalance
no_of_previous_cancellations is highly skewed (γ1 = 25.12430521)Skewed
no_of_weekend_nights has 5903 (46.5%) zerosZeros
no_of_week_nights has 849 (6.7%) zerosZeros
room_type_reserved has 9833 (77.5%) zerosZeros
lead_time has 463 (3.6%) zerosZeros
no_of_previous_cancellations has 12587 (99.1%) zerosZeros
no_of_previous_bookings_not_canceled has 12409 (97.7%) zerosZeros
avg_price_per_room has 193 (1.5%) zerosZeros
no_of_special_requests has 6897 (54.3%) zerosZeros

Reproduction

Analysis started2023-03-13 03:07:20.295971
Analysis finished2023-03-13 03:07:28.766965
Duration8.47 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

no_of_adults
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
2
9199 
1
2623 
3
 
822
0
 
47
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

Length

2023-03-12T20:07:28.803966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:28.870966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9199
72.5%
1 2623
 
20.7%
3 822
 
6.5%
0 47
 
0.4%
4 4
 
< 0.1%

no_of_children
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
0
11733 
1
 
575
2
 
379
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

Length

2023-03-12T20:07:28.929966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:28.994964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11733
92.4%
1 575
 
4.5%
2 379
 
3.0%
3 8
 
0.1%

no_of_weekend_nights
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80913746
Minimum0
Maximum7
Zeros5903
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.043964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.87011098
Coefficient of variation (CV)1.0753562
Kurtosis0.50021729
Mean0.80913746
Median Absolute Deviation (MAD)1
Skewness0.76285186
Sum10272
Variance0.75709312
MonotonicityNot monotonic
2023-03-12T20:07:29.094964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5903
46.5%
1 3523
27.8%
2 3148
24.8%
3 64
 
0.5%
4 34
 
0.3%
5 14
 
0.1%
6 8
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 5903
46.5%
1 3523
27.8%
2 3148
24.8%
3 64
 
0.5%
4 34
 
0.3%
5 14
 
0.1%
6 8
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 8
 
0.1%
5 14
 
0.1%
4 34
 
0.3%
3 64
 
0.5%
2 3148
24.8%
1 3523
27.8%
0 5903
46.5%

no_of_week_nights
Real number (ℝ)

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2029145
Minimum0
Maximum17
Zeros849
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.159964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4198644
Coefficient of variation (CV)0.64453902
Kurtosis9.3228691
Mean2.2029145
Median Absolute Deviation (MAD)1
Skewness1.735608
Sum27966
Variance2.0160148
MonotonicityNot monotonic
2023-03-12T20:07:29.215965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 4026
31.7%
1 3294
25.9%
3 2748
21.6%
4 1049
 
8.3%
0 849
 
6.7%
5 550
 
4.3%
6 67
 
0.5%
7 38
 
0.3%
10 22
 
0.2%
8 18
 
0.1%
Other values (8) 34
 
0.3%
ValueCountFrequency (%)
0 849
 
6.7%
1 3294
25.9%
2 4026
31.7%
3 2748
21.6%
4 1049
 
8.3%
5 550
 
4.3%
6 67
 
0.5%
7 38
 
0.3%
8 18
 
0.1%
9 14
 
0.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
16 1
 
< 0.1%
15 5
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 3
 
< 0.1%
11 4
 
< 0.1%
10 22
0.2%
9 14
0.1%
8 18
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
0
9810 
1
1745 
2
1137 
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%

Length

2023-03-12T20:07:29.280964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:29.347964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9810
77.3%
1 1745
 
13.7%
2 1137
 
9.0%
3 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
0
12280 
1
 
415

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

Length

2023-03-12T20:07:29.400964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:29.463965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12280
96.7%
1 415
 
3.3%

room_type_reserved
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33800709
Minimum0
Maximum6
Zeros9833
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.509005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77501412
Coefficient of variation (CV)2.2928931
Kurtosis11.016307
Mean0.33800709
Median Absolute Deviation (MAD)0
Skewness3.0809864
Sum4291
Variance0.60064689
MonotonicityNot monotonic
2023-03-12T20:07:29.557964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9833
77.5%
1 2129
 
16.8%
3 351
 
2.8%
2 240
 
1.9%
4 84
 
0.7%
5 55
 
0.4%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 9833
77.5%
1 2129
 
16.8%
2 240
 
1.9%
3 351
 
2.8%
4 84
 
0.7%
5 55
 
0.4%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 55
 
0.4%
4 84
 
0.7%
3 351
 
2.8%
2 240
 
1.9%
1 2129
 
16.8%
0 9833
77.5%

lead_time
Real number (ℝ)

Distinct340
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.579913
Minimum0
Maximum443
Zeros463
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.625964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q116
median57
Q3127
95-th percentile275
Maximum443
Range443
Interquartile range (IQR)111

Descriptive statistics

Standard deviation87.294409
Coefficient of variation (CV)1.0200339
Kurtosis1.1575297
Mean85.579913
Median Absolute Deviation (MAD)47
Skewness1.3011756
Sum1086437
Variance7620.3139
MonotonicityNot monotonic
2023-03-12T20:07:29.698963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
 
3.6%
1 374
 
2.9%
2 234
 
1.8%
4 234
 
1.8%
3 220
 
1.7%
5 191
 
1.5%
6 188
 
1.5%
8 166
 
1.3%
7 154
 
1.2%
12 143
 
1.1%
Other values (330) 10328
81.4%
ValueCountFrequency (%)
0 463
3.6%
1 374
2.9%
2 234
1.8%
3 220
1.7%
4 234
1.8%
5 191
1.5%
6 188
1.5%
7 154
 
1.2%
8 166
 
1.3%
9 122
 
1.0%
ValueCountFrequency (%)
443 9
 
0.1%
433 8
 
0.1%
418 23
0.2%
386 23
0.2%
381 1
 
< 0.1%
377 29
0.2%
361 1
 
< 0.1%
359 7
 
0.1%
353 1
 
< 0.1%
352 1
 
< 0.1%

arrival_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
2018
10415 
2017
2280 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters50780
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2017
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 10415
82.0%
2017 2280
 
18.0%

Length

2023-03-12T20:07:29.764963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:29.826963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 10415
82.0%
2017 2280
 
18.0%

Most occurring characters

ValueCountFrequency (%)
2 12695
25.0%
0 12695
25.0%
1 12695
25.0%
8 10415
20.5%
7 2280
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50780
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12695
25.0%
0 12695
25.0%
1 12695
25.0%
8 10415
20.5%
7 2280
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 50780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12695
25.0%
0 12695
25.0%
1 12695
25.0%
8 10415
20.5%
7 2280
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12695
25.0%
0 12695
25.0%
1 12695
25.0%
8 10415
20.5%
7 2280
 
4.5%

arrival_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4315872
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.876963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0842425
Coefficient of variation (CV)0.41501801
Kurtosis-0.96157185
Mean7.4315872
Median Absolute Deviation (MAD)2
Skewness-0.34099091
Sum94344
Variance9.5125519
MonotonicityNot monotonic
2023-03-12T20:07:29.931963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 1879
14.8%
9 1623
12.8%
8 1282
10.1%
6 1107
8.7%
12 1085
8.5%
11 1055
8.3%
4 1015
8.0%
7 968
7.6%
5 917
7.2%
3 821
6.5%
Other values (2) 943
7.4%
ValueCountFrequency (%)
1 346
 
2.7%
2 597
 
4.7%
3 821
6.5%
4 1015
8.0%
5 917
7.2%
6 1107
8.7%
7 968
7.6%
8 1282
10.1%
9 1623
12.8%
10 1879
14.8%
ValueCountFrequency (%)
12 1085
8.5%
11 1055
8.3%
10 1879
14.8%
9 1623
12.8%
8 1282
10.1%
7 968
7.6%
6 1107
8.7%
5 917
7.2%
4 1015
8.0%
3 821
6.5%

arrival_date
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.743127
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:29.995963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7659164
Coefficient of variation (CV)0.55680909
Kurtosis-1.1698083
Mean15.743127
Median Absolute Deviation (MAD)8
Skewness0.012229463
Sum199859
Variance76.84129
MonotonicityNot monotonic
2023-03-12T20:07:30.060964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 499
 
3.9%
17 466
 
3.7%
2 457
 
3.6%
4 456
 
3.6%
19 456
 
3.6%
16 450
 
3.5%
30 438
 
3.5%
20 429
 
3.4%
6 428
 
3.4%
25 427
 
3.4%
Other values (21) 8189
64.5%
ValueCountFrequency (%)
1 383
3.0%
2 457
3.6%
3 386
3.0%
4 456
3.6%
5 391
3.1%
6 428
3.4%
7 378
3.0%
8 405
3.2%
9 398
3.1%
10 409
3.2%
ValueCountFrequency (%)
31 214
1.7%
30 438
3.5%
29 407
3.2%
28 417
3.3%
27 393
3.1%
26 416
3.3%
25 427
3.4%
24 389
3.1%
23 358
2.8%
22 370
2.9%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
1
8117 
0
3713 
2
 
693
4
 
137
3
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

Length

2023-03-12T20:07:30.126964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:30.363978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8117
63.9%
0 3713
29.2%
2 693
 
5.5%
4 137
 
1.1%
3 35
 
0.3%

repeated_guest
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
0
12375 
1
 
320

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

Length

2023-03-12T20:07:30.423977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:30.486978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12375
97.5%
1 320
 
2.5%

no_of_previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022371012
Minimum0
Maximum13
Zeros12587
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:30.531976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35543083
Coefficient of variation (CV)15.888008
Kurtosis740.84076
Mean0.022371012
Median Absolute Deviation (MAD)0
Skewness25.124305
Sum284
Variance0.12633107
MonotonicityNot monotonic
2023-03-12T20:07:30.592978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12587
99.1%
1 56
 
0.4%
3 18
 
0.1%
2 16
 
0.1%
11 8
 
0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
0 12587
99.1%
1 56
 
0.4%
2 16
 
0.1%
3 18
 
0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
11 8
 
0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 8
 
0.1%
6 1
 
< 0.1%
5 3
 
< 0.1%
4 5
 
< 0.1%
3 18
 
0.1%
2 16
 
0.1%
1 56
 
0.4%
0 12587
99.1%

no_of_previous_bookings_not_canceled
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15872391
Minimum0
Maximum58
Zeros12409
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:30.663977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7886424
Coefficient of variation (CV)11.268891
Kurtosis433.41322
Mean0.15872391
Median Absolute Deviation (MAD)0
Skewness18.801114
Sum2015
Variance3.1992415
MonotonicityNot monotonic
2023-03-12T20:07:30.732977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 12409
97.7%
1 76
 
0.6%
2 39
 
0.3%
3 30
 
0.2%
4 24
 
0.2%
5 17
 
0.1%
8 14
 
0.1%
6 11
 
0.1%
7 10
 
0.1%
10 7
 
0.1%
Other values (30) 58
 
0.5%
ValueCountFrequency (%)
0 12409
97.7%
1 76
 
0.6%
2 39
 
0.3%
3 30
 
0.2%
4 24
 
0.2%
5 17
 
0.1%
6 11
 
0.1%
7 10
 
0.1%
8 14
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
58 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
48 1
< 0.1%
47 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
41 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%

avg_price_per_room
Real number (ℝ)

Distinct2203
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.51919
Minimum0
Maximum375.5
Zeros193
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:30.814977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q180.1
median99.45
Q3120.6
95-th percentile166
Maximum375.5
Range375.5
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation35.370949
Coefficient of variation (CV)0.34168496
Kurtosis2.3580546
Mean103.51919
Median Absolute Deviation (MAD)20.4
Skewness0.60462227
Sum1314176.1
Variance1251.1041
MonotonicityNot monotonic
2023-03-12T20:07:30.892977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 321
 
2.5%
65 312
 
2.5%
90 250
 
2.0%
115 242
 
1.9%
95 236
 
1.9%
120 221
 
1.7%
110 210
 
1.7%
0 193
 
1.5%
100 192
 
1.5%
85 167
 
1.3%
Other values (2193) 10351
81.5%
ValueCountFrequency (%)
0 193
1.5%
1 3
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
6 10
 
0.1%
6.5 1
 
< 0.1%
6.67 1
 
< 0.1%
9 2
 
< 0.1%
12 7
 
0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
375.5 1
< 0.1%
314.1 1
< 0.1%
300 2
< 0.1%
296 1
< 0.1%
279.2 1
< 0.1%
278.9 1
< 0.1%
265.44 1
< 0.1%
264.1 1
< 0.1%
263.55 1
< 0.1%
260.9 1
< 0.1%

no_of_special_requests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62528555
Minimum0
Maximum5
Zeros6897
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size714.4 KiB
2023-03-12T20:07:30.960977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79090482
Coefficient of variation (CV)1.2648698
Kurtosis0.86576038
Mean0.62528555
Median Absolute Deviation (MAD)0
Skewness1.1382346
Sum7938
Variance0.62553043
MonotonicityNot monotonic
2023-03-12T20:07:31.029082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6897
54.3%
1 3962
31.2%
2 1568
 
12.4%
3 235
 
1.9%
4 30
 
0.2%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 6897
54.3%
1 3962
31.2%
2 1568
 
12.4%
3 235
 
1.9%
4 30
 
0.2%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 30
 
0.2%
3 235
 
1.9%
2 1568
 
12.4%
1 3962
31.2%
0 6897
54.3%

booking_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size714.4 KiB
0
8555 
1
4140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12695
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Length

2023-03-12T20:07:31.097083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-12T20:07:31.172086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Most occurring characters

ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12695
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8555
67.4%
1 4140
32.6%

Interactions

2023-03-12T20:07:27.815967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.180971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.189970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.896969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.561968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.229968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.993966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.669964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.347963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.016968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.878967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.253971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.263971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.967969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.630969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.397966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.068965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.740966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.418964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.218968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.945967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.332970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.337969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.033969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.697968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.467967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.139965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.819965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.487963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.289968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.008967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.402971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.409970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.098969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.763968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.534967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.207965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.887965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.554964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.358968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.065967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.466971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.476969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.159969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.822967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.596968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.271966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.948965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.614964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.419968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.125967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.534971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.545970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.222969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.885968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.661967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.336966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.010965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.679962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.482968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.189967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.602972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.616969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.286967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.950968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.727966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.401966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.074965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.746964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.550967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.255967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.669971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.686969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.355968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.022968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.789966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.469965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.142965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.816964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.618968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.322967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:21.735970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.758970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.428968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.089966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.858966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.536966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.208965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.879964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.683968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:28.392967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.123970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:22.829969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:23.497969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.164968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:24.927967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:25.604965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.279964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:26.949964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-12T20:07:27.751967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-12T20:07:31.236086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
no_of_weekend_nightsno_of_week_nightsroom_type_reservedlead_timearrival_montharrival_dateno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsno_of_adultsno_of_childrentype_of_meal_planrequired_car_parking_spacearrival_yearmarket_segment_typerepeated_guestbooking_status
no_of_weekend_nights1.0000.0170.0740.106-0.0140.022-0.023-0.057-0.0280.0670.0670.0280.0430.0110.0680.1160.0560.080
no_of_week_nights0.0171.0000.1120.2400.062-0.012-0.041-0.1250.0320.0500.0780.0220.0680.0630.0360.1220.1220.105
room_type_reserved0.0740.1121.000-0.049-0.0070.039-0.021-0.0240.3740.1410.3060.4730.1590.0400.0990.1750.0720.048
lead_time0.1060.240-0.0491.0000.081-0.000-0.096-0.194-0.028-0.0900.1030.0370.1730.0780.1500.1770.1630.440
arrival_month-0.0140.062-0.0070.0811.000-0.0360.0120.0010.0130.0800.0920.0680.0970.0480.3930.1010.0630.180
arrival_date0.022-0.0120.039-0.000-0.0361.000-0.010-0.0010.0020.0260.0420.0260.0700.0190.0810.0450.0290.027
no_of_previous_cancellations-0.023-0.041-0.021-0.0960.012-0.0101.0000.419-0.098-0.0300.0370.0000.0000.0420.0140.1170.3980.038
no_of_previous_bookings_not_canceled-0.057-0.125-0.024-0.1940.001-0.0010.4191.000-0.177-0.0010.0650.0000.0000.0910.0150.1620.5530.054
avg_price_per_room-0.0280.0320.374-0.0280.0130.002-0.098-0.1771.0000.2000.1950.3160.1190.0760.2040.4070.2280.171
no_of_special_requests0.0670.0500.141-0.0900.0800.026-0.030-0.0010.2001.0000.1160.1020.0680.0910.0940.2070.0300.254
no_of_adults0.0670.0780.3060.1030.0920.0420.0370.0650.1950.1161.0000.2140.0880.0250.1040.1970.2340.110
no_of_children0.0280.0220.4730.0370.0680.0260.0000.0000.3160.1020.2141.0000.0500.0260.0630.0990.0340.042
type_of_meal_plan0.0430.0680.1590.1730.0970.0700.0000.0000.1190.0680.0880.0501.0000.0360.1850.2350.0720.087
required_car_parking_space0.0110.0630.0400.0780.0480.0190.0420.0910.0760.0910.0250.0260.0361.0000.0000.1220.1160.089
arrival_year0.0680.0360.0990.1500.3930.0810.0140.0150.2040.0940.1040.0630.1850.0001.0000.1820.0150.178
market_segment_type0.1160.1220.1750.1770.1010.0450.1170.1620.4070.2070.1970.0990.2350.1220.1821.0000.4750.150
repeated_guest0.0560.1220.0720.1630.0630.0290.3980.5530.2280.0300.2340.0340.0720.1160.0150.4751.0000.107
booking_status0.0800.1050.0480.4400.1800.0270.0380.0540.1710.2540.1100.0420.0870.0890.1780.1500.1071.000

Missing values

2023-03-12T20:07:28.497965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-12T20:07:28.673965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

no_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
1594620230014520181124100096.9020
1213220120002732018513000095.0001
2172002000682017929000065.0000
3858200300141201824100082.4501
179082011000159201849000070.0010
12018202210011020186171000109.6510
2477201300028520181030000100.0001
182410010001201792200065.0000
132972041000115220181024000080.3901
1262221210111312018721000121.5010
no_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
34812013000752018317100080.0001
111110120029201812171000130.0000
942102000022017920000077.0000
56642012003302018561000198.9020
962003000332018928000085.5000
134352012004942018325100095.2500
77632004001332018223100061.6010
153772014001342018441000131.4001
1773020020001220171216000058.0000
1572510120000201783200065.0000

Duplicate rows

Most frequently occurring

no_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status# duplicates
8410020001922018624000095.00034
11310030007120186140000120.00034
404200300037201810130000105.00029
78100200016420171020000100.00026
61920120003052018114000089.00126
118100300016620181110000110.00124
44720030003042018113000089.00124
2502001000562018680000120.00023
2822001200552018460000104.00022
3912002200377201810140000115.01122